Exclusive Neural Network Representation of the Quasi-Diabatic Hamiltonians Including Conical Intersections

2020 ◽  
Vol 11 (18) ◽  
pp. 7552-7558 ◽  
Author(s):  
Yingyue Hong ◽  
Zhengxi Yin ◽  
Yafu Guan ◽  
Zhaojun Zhang ◽  
Bina Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Conical Intersections ◽  
Network Representation

Convolutional Neural Network Representation of the Total Multiple Scattering Cross Section

2021 ◽  
Author(s):  
Thang Tran ◽  
Peter Lai ◽  
Wei-Ching Wang ◽  
Amaris Rosa ◽  
Feruza Amirkulova ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Multiple Scattering ◽  
Cross Section ◽  
Scattering Cross Section ◽  
Scattering Cross ◽  
Network Representation

Neural network representation and optimization of thermoelectric states of multiple interacting quantum dots

2020 ◽  
Vol 22 (28) ◽  
pp. 16165-16173
Author(s):  
Hangbo Zhou ◽  
Gang Zhang ◽  
Yong-Wei Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Quantum Dots ◽  
Seebeck Coefficient ◽  
Master Equation ◽  
Thermoelectric Properties ◽  
Electrical Conductance ◽  
Thermal Conductance ◽  
Quantum Master Equation ◽  
Network Representation

We perform quantum master equation calculations and machine learning to investigate the thermoelectric properties of multiple interacting quantum dots, including electrical conductance, Seebeck coefficient, thermal conductance and ZT.

scholarly journals Identifying Disease Related Genes by Network Representation and Convolutional Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Bolin Chen ◽  
Yourui Han ◽  
Xuequn Shang ◽  
Shenggui Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Biological Networks ◽  
Dimensional Space ◽  
Scale Free ◽  
Network Representation ◽  
Disease Related Genes ◽  
Representation Method ◽  
Low Dimensional

The identification of disease related genes plays essential roles in bioinformatics. To achieve this, many powerful machine learning methods have been proposed from various computational aspects, such as biological network analysis, classification, regression, deep learning, etc. Among them, deep learning based methods have gained big success in identifying disease related genes in terms of higher accuracy and efficiency. However, these methods rarely handle the following two issues very well, which are (1) the multifunctions of many genes; and (2) the scale-free property of biological networks. To overcome these, we propose a novel network representation method to transfer individual vertices together with their surrounding topological structures into image-like datasets. It takes each node-induced sub-network as a represented candidate, and adds its environmental characteristics to generate a low-dimensional space as its representation. This image-like datasets can be applied directly in a Convolutional Neural Network-based method for identifying cancer-related genes. The numerical experiments show that the proposed method can achieve the AUC value at 0.9256 in a single network and at 0.9452 in multiple networks, which outperforms many existing methods.

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